E
NHANCING
B
RAND
E
QUITY
T
HROUGH
F
LOW AND
T
ELEPRESENCE
:
A
C
OMPARISON OF
2D
AND
3D
V
IRTUAL
W
ORLDS
Fiona Fui-Hoon Nah
College of Business Administration, University of Nebraska–Lincoln, Lincoln, NE 68588 U.S.A. {fnah@unlnotes.unl.edu}
Brenda Eschenbrenner
College of Business and Technology, University of Nebraska at Kearney, Kearney, NE 68849 U.S.A. {eschenbrenbl@unk.edu}
David DeWester
College of Arts and Sciences, University of Nebraska–Lincoln, Lincoln, NE 68588 U.S.A. {ddewester2@unl.edu}
Appendix A
Summary of Literature on Flow and Telepresence in Online Environments
Reference/ Authors Antecedents Online Experience Direct Consequences Indirect Consequences Research Setting Research Method Steuer (1992) Vividness, Interactivity
Telepresence Virtual Reality Conceptual
Trevino and Webster (1992)
Tech. Type, Tech. Characteristic (Ease of Use), Ind. Diff. (Computer Skill) Flow – Control, Attention Focus, Curiosity, Intrinsic Interest Attitude, Effectiveness, Quantity, Barrier Reduction Computer-mediated Communication (E-mail, Voice Mail)
Survey Webster et al. (1993) Software Characteristics (Flexibility, Modifiability) Flow – Control, Attention Focus, Cognitive Enjoyment (Curiosity and Intrinsic Interest) Exploratory Behavior (Experimentation), System Use, Perceived Comm. Quantity, Perceived Software Usage in Work Setting Survey
Reference/ Authors Antecedents Online Experience Direct Consequences Indirect Consequences Research Setting Research Method Hoffman and Novak (1996) Control Char. (Skills, Challenges), Content Char. (Interactivity, Vividness), Process Char. (Goal-Directed, Experiential), Involvement, Focused Attention, Telepresence
Flow Consumer Learning,
Perceived Behavioral Control, Exploratory Behavior, Positive Subjective Experience Hypermedia Computer-mediated Environment Conceptual Lombard and Ditton (1997) Media Form (Vividness or Sensory Richness), Media Content (e.g., Task or Activity), Media User Variables Presence (or Telepresence) Arousal, Enjoyment, Involvement, Task Performance, Skills Training, Desensitization, Persuasion, Memory, Social Judgment, Parasocial Interaction/ Relationships
Virtual Environment Conceptual
Chen et al. (1999) Clear Goals, Immediate Feedback, Matched Skills and Challenges Flow – Merger of Action and Awareness, Concentration, Sense of Control Self-consciousness, Time Distortion, Autotelic Experience
Web Navigation Survey
Nel et al. (1999)
Web Site Type (Content, Audience Focus) Flow – Control, Attention Focus, Curiosity, Intrinsic Interest
Website Revisit Web Sites Experiment
Agarwal and Karahanna (2000) Personal Innovativeness, Playfulness Cognitive Absorption/Flow – Curiosity, Control, Temporal Dissociation, Focused Immersion, Heightened Enjoyment Perceived Ease of Use, Perceived Usefulness, Behavioral Intention IT (World Wide Web) Usage Survey Chen et al. (2000) Flow – Merger of Action and Awareness, Concentration, Loss of Self Consciousness, Time Distortion, Sense of Control, Telepresence, Enjoyment, Perceived Challenges
Reference/ Authors Antecedents Online Experience Direct Consequences Indirect Consequences Research Setting Research Method Novak et al. (2000) Skill/Control, Interactive Speed, Importance, Challenge/Arousal, Focused Attention, Telepresence/Time Distortion
Flow Online Shopping Survey
Rettie (2001) Goals, Feedback, Skills, Challenge Flow – Merging of Action and Awareness, Focused Concentration, Sense of Control, Loss of Self Consciousness, Time Distortion, Autotelic Experience
Internet Use Focused groups Koufaris (2002) Product Involvement, Web Skills, Value-Added Search Mechanisms, Challenges Flow – Shopping Enjoyment, Concentration
Intention to Return Online Shopping Survey
Luna et al. (2002) Balance of Challenges/Skills, Perceived Control, Unambiguous Demands, Focused Attention, Attitude toward Site
Flow Revisit Intent,
Purchase Intent
Purchase Online Shopping Experiment
Huang (2003) Complexity, Novelty, Interactivity Flow – Control, Attention, Curiosity, Interest Utilitarian Performance, Hedonic Performance
Web Sites Survey
Klein (2003) Media Richness, User Control
Telepresence Persuasion (Belief Strength, Attitude Intensity) Computer–mediated Environment Experiment Korzaan (2003) Flow Exploratory Behavior, Attitude Intention to Purchase
Online Shopping Survey Luna et al.
(2003)
Attention, Challenge, Interactivity, Attitude toward Site
Flow Purchase Intent,
Revisit Intent
Online Shopping Survey
Novak et al. (2003) Goal-directed vs. Experiential Activities, Skill, Challenge, Novelty, Importance
Reference/ Authors Antecedents Online Experience Direct Consequences Indirect Consequences Research Setting Research Method Hsu and Lu (2004) Perceived Ease of Use
Flow Intention Online Games Survey
Jiang and Benbasat (2004) Visual Control, Functional Control Flow – Control, Attention Focus, Cognitive Enjoyment E-commerce Websites Experiment
Pace (2004) Goals and Navigation Behavior, Challenge and Skills, Attention Flow – Joy of Discovery, Reduced Awareness of Irrelevant Factors, Distorted Sense of Time, Merging of Action and Awareness, Sense of Control, Mental Alertness, Telepresence
Web Browsing Grounded Theory (Theoretical Sampling, Semi-Structured Interviews) Pilke (2004) Immediate Feedback, Clear Rules/Goals, Complexity, Dynamic Challenges
Flow World Wide Web Interviews
Reid (2004) Cognitive Ability, Volitional Control, Self-efficacy Flow and Playfulness Competence, Creativity, User Satisfaction
Virtual Reality Interviews, Experiment, Observation Skadberg and Kimmel (2004) Speed, Ease of Use, Attractiveness, Interactivity, Domain Knowledge/Skill, Information in the Web Site/Challenge Flow – Enjoyment, Time Distortion, Telepresence
Increased Learning Change of Attitude and Behavior
Web Browsing Survey
Kim et al. (2005)
Skills, Challenges, Focused Attention
Flow Online Games Survey
Saade and Bahli (2005) Cognitive Absorption/Flow – Temporal Dissociation, Focused Immersion, Heightened Enjoyment Perceived Ease of Use, Perceived Usefulness
Intention to Use Internet Learning Survey
Siekpe (2005) Flow – Challenges, Concentration, Curiosity, Control Intention to Purchase, Intention to Return
Online Shopping Survey
Suh and Lee (2005)
(Virtually High versus Low) Experiential Products
Telepresence Product Knowledge, Attitude, Purchase Intentions
Reference/ Authors Antecedents Online Experience Direct Consequences Indirect Consequences Research Setting Research Method
Chen (2006) Clear Goal, Potential Control, Immediate Feedback, Merger of Action and Awareness Flow – Telepresence, Time Distortion, Concentration, Loss of Self-consciousness Positivity of Affects, Enjoyable Feeling
Web Browsing Survey (Digitalized Experience Sampling Method) Li and Browne (2006)
Need for Cognition, Mood Flow – Focused Attention, Control, Curiosity, Temporal Dissociation
Online Experience Survey
Shin (2006) Skill, Challenge, Individual Differences Flow – Enjoyment, Telepresence, Focused Attention, Engagement, Time Distortion Achievement, Satisfaction Virtual Learning Environment Survey Tung et al. (2006) Product Involvement Flow – Control, Attention, Curiosity, Interest
Mood, Attitude Web Site
Advertising Experiment Choi et al. (2007) Learning Interface, Interaction, Instructor Attitude, Content
Flow Attitude Toward
E-learning, Learning Outcomes (Tech. Self-efficacy) E-learning Survey Chang and Wang (2008) Interactivity, Perceived Ease of Use Flow Perceived Usefulness, Attitude toward Use, Behavioral Intention Computer-mediated Communication Survey Chen et al. (2008) Flow – Control, Attention Focus, Curiosity and Interest Communication Outcome – Effectiveness, Quality, Volume Computer-mediated Communication Experiment Park et al. (2008) Control Char. (Skills, Challenges), Content Char. (Interactivity, Vividness), Process Char. (Extrinsic/Intrinsic Motivation)
Flow Brand Equity Virtual Worlds Conceptual
Guo and Poole (2009) Website Complexity, Clear Goal, Immediate Feedback, Congruence of Challenge and Skill
Flow – Concen-tration, Control, Mergence of Action and Awareness, Transformation of Time, Transcendence of Self, Autotelic Experience
Reference/ Authors Antecedents Online Experience Direct Consequences Indirect Consequences Research Setting Research Method Hoffman and Novak (2009) Skill, Challenge, Interactivity, Telepresence, Attractiveness, Novelty, Playfulness, Personal Innovativeness, Content/Interface, Ease of Use, Positive Subjective Experience/Attitude Flow Learning, Control/Perceived Behavioral Control, Exploratory Behavior/Curiosity/ Discovery, Positive Subjective Experience/Attitude, Ease of Use, Perceived Usefulness, Purchase/ Behavioral Intention, Addictive Behavior
Purchase/Use Internet Conceptual
Shin (2009) Perceived Synchronicity
Flow Intention Virtual Worlds Survey
Ho and Kuo (2010)
Computer Attitudes Flow – Control, Focused Attention, Intrinsic Interest, Curiosity Learning Outcomes – Adaptation, Replication, Innovation E-learning Survey Lee and Chen (2010) Flow – Concentration, Enjoyment, Time Distortion, Telepresence Attitude, Controllability, Self-efficacy, Perceived Ease of Use Perceived Behavioral Control, Intention, Behavior, Perceived Usefulness
Online Shopping Survey
Nah et al (2010)
Balance of Skills and Challenges
Flow Brand Equity Behavioral
Intention
3D Virtual World Survey Zaman et al (2010) Telepresence, Perceived Control Flow – Enjoyment, Concentration Positive Affect, Exploratory Behavior Perceived Expected Creativity
Appendix B
Items for Measures
Item
Construct and Measurement Items
Telepresence
(7-point Likert scale)
TP1
I forgot about my immediate surroundings when I was navigating the <hospital brand name> virtual tour.
TP2
When the virtual tour ended, I felt like I came back to the “real world” after a journey.
TP3
During the virtual tour, I forgot that I was in the middle of an experiment.
TP4
The computer-generated world seemed to be “somewhere I visited” rather than “something I saw.”
Enjoyment
(7-point Likert scale)
ENJ1
I found my virtual tour of <hospital brand name> enjoyable.
ENJ2
I found my virtual tour of <hospital brand name> boring. (Reverse coded)
ENJ3
I found my virtual tour of <hospital brand name> interesting.
ENJ4
I found my virtual tour of <hospital brand name> fun.
Brand Equity
(7-point Likert scale)
BE1
Even if another hospital offers the same quality of services as <hospital brand name>, I would prefer to use
the services of <hospital brand name>.
BE2
If there is another hospital as good as <hospital brand name>, I prefer to go to <hospital brand name>.
BE3
It makes sense to use the services of <hospital brand name> instead of services of any other hospitals even
if they are the same.
Behavioral Intention (i.e., intention to visit hospital)
(7-point Likert scale)
The header for the three intention items read: “Assuming that <hospital brand name> is available in your
area…”
INT1
…I would consider <hospital brand name> the next time I need a hospital service.
INT2
…I would recommend <hospital brand name> if a friend calls me to get my advice in his/her search for a
hospital.
Appendix C
Descriptive Statistics, Reliability, Validity, and Common Method Variance
Descriptive Statistics
Table C1. Descriptive Statistics of Indicators
Items
Aggregate Data
2D Condition
3D Condition
Mean
Standard
Deviation
Mean
Standard
Deviation
Mean
Standard
Deviation
INT1
4.45
1.65
4.30
1.66
4.56
1.64
INT2
4.42
1.61
4.22
1.65
4.58
1.57
INT3
4.03
1.69
3.73
1.70
4.26
1.66
BE1
4.30
1.35
4.32
1.29
4.30
1.39
BE2
4.24
1.31
4.13
1.21
4.31
1.37
BE3
4.46
1.38
4.39
1.32
4.50
1.42
ENJ1
4.78
1.30
4.36
1.41
5.05
1.14
ENJ2
4.29
1.59
3.68
1.50
4.69
1.53
ENJ3
5.00
1.30
4.66
1.47
5.21
1.13
ENJ4
4.52
1.39
4.02
1.44
4.84
1.25
TP1
3.48
1.67
3.01
1.58
3.79
1.65
TP2
3.37
1.66
2.95
1.53
3.64
1.69
TP3
3.27
1.61
3.12
1.66
3.36
1.58
TP4
3.69
1.61
3.31
1.59
3.94
1.57
INT: Behavioral Intention; BE: Brand Equity; ENJ: Enjoyment; TP: Telepresence
Skewness and Kurtosis
Based on Kline (2005), we examined the ratio of the unstandardized skewness and kurtosis indices divided by their standard error. Kline
suggests that values less than 10 indicate no serious skewness or kurtosis problem. ENJ3 is the only item with skewness of -10.3 that exceeds
this threshold. Although ENJ3’s kurtosis value was below 10, it was higher than any other item and so, as an extra check on whether or not
skewness and kurtosis were affecting our results, we reestimated our model after deleting ENJ3 and found that it did not result in any significant
change to the fit indices (i.e., CFI went from 0.978 to 0.982, resulting in a change of only 0.004). The paths and R²s also did not change much.
Therefore, skewness and kurtosis problems were not deemed to be significant problems in our data.
Convergent and Discriminant Validity
Convergent validity was assessed using several methods. First, a general rule states that in the SEM model, the loading of each indicator on
its construct should have a path weight of at least 0.7 (see Hulland 1999). The weights in our measurement model range from 0.75 to 0.96.
Second, to assess reliability, we used Omega coefficients in Mplus (see Raykov and Marcoulides 2010). This model-based reliability statistic
is very similar to Cronbach’s Alpha and is interpreted similarly (e.g., greater than 0.8 as recommended by Cohen 1988), but is computed using
model parameters—specifically the loadings for the indicators on their corresponding latent constructs as well as the indicators’ residual
variances—in order to give a model-specific measure of reliability. Table C2 shows the Omega coefficient for each of the latent constructs.
Third, Fornell and Larcker (1981) recommend that all average variance extracted (AVE) be greater than 0.5; our model’s smallest AVE is 0.65,
which is shown in Table C3 as its square root of 0.80. Hence, the statistics show that there is strong convergent validity in the data.
Table C2. Omega Coefficients for Constructs
Construct
Omega
Behavioral Intention
0.95
Brand Equity
0.99
Enjoyment
0.99
Telepresence
0.88
Table C3. Correlations of Constructs and Average Variances
Extracted
Construct
INT
BE
ENJ
TP
Behavioral Intention
0.92
Brand Equity
0.49
0.88
Enjoyment
0.54
0.43
0.88
Telepresence
0.50
0.40
0.65
0.80
(*Diagonal represents Square Root of Average Variance Extracted)Discriminant validity is demonstrated in two ways. First, the AVE rule from Chin et al. (2003) states that the square root of the AVE for each
of the constructs should be greater than that construct’s correlations with the other constructs. As shown in Table C3, the smallest square root
of AVE is 0.80, which is larger than any of the interconstruct correlations. In addition, discriminant validity can be shown through pairwise
factor analysis. To conduct this test, items are taken from each pair of constructs and placed in an EFA in Mplus. The same items are then
placed in a CFA with one factor. If there is a significant chi-square difference, then the items do not load on one factor, which implies that
the two constructs are not actually a single construct. The chi-square difference for each test is significant at the .001 level, which implies that
each pair of two constructs is distinct from one another.
Common Method Variance Test
Common method variance is a phenomenon that occurs when items are artificially correlated with each other (Podsakoff et al. 2003; Podsakoff
and Organ 1986). One method to detect common method variance is to conduct a series of confirmatory factor analyses on the items and force
the number of factors to be one, and then iteratively adding a factor until reaching four factors. If common method variance is present, we
would expect items from different constructs to be more highly correlated with each other and load together. If the result is fewer than the four
factors we expect to obtain, it suggests common method variance. Four CFAs were carried out in Mplus in the following order: one-factor,
two-factor, three-factor, and four-factor. Each model had better fit statistics than the previous model and the chi-square difference test was
significant between each pair of models. This implies that the data loads on four factors, which suggests that the items are not artificially
correlated with each other.
Straub et al. (2004) suggest that reliability coefficients that are
too high
are just as problematic as reliability coefficients that are
too low
when
items are presented in blocks (as in this case). In spite of scale item blocking potentially creating common method variance, it was not sufficient
to fail the common method variance test.
Appendix D
Mediator Tests
We carried out six mediator tests using Baron and Kenney’s (1986) four-step procedures to assess the mediating effects in Figure 2.
(1) Telepresence as a mediator of the relationship between 2D/3D and Enjoyment
(2) Enjoyment as a mediator of the relationship between Telepresence and Brand Equity
(3) Brand Equity as a mediator of the relationship between Enjoyment and Behavioral Intention
(4) Enjoyment as a mediator of the relationship between Telepresence and Behavioral Intention
(5) Brand Equity as a mediator of the relationship between Telepresence and Behavioral Intention
(6) Telepresence and Enjoyment as mediators of the relationship between 2D/3D and Brand Equity
Telepresence as a Mediator of the Relationship Between 2D/3D and Enjoyment
Results from Steps 1 and 2 Results from Steps 3 and 4 Overall ResultsSatisfied
The bivariate relationships are significant:
(i) 2D/3D and Telepresence (r = .204, p < .001) and (ii) 2D/3D and Enjoyment (r = .292, p < .001).
Satisfied
Enjoyment = b0 + b1 × (2D/3D) + b2 × Telepresence + e Step 3 is satisfied with b2 = .512 (beta = .565, p < .001) and step 4 is satisfied with b1 = .460 (beta = .177, p < .001). 2D/3D r = .292, p < .001 r = .204, p < .001 Steps 1 and 2 2D/3D Telepresence Enjoyment Enjoyment Telepresence 2D/3D b=.460, ß=.177, p < .001 b=.512, ß=.565, p < .001 Steps 3 and 4
Enjoyment as a Mediator of the Relationship Between Telepresence and Brand Equity
Results from Steps 1 and 2 Results from Steps 3 and 4 Overall ResultsSatisfied
2D Condition – Satisfied
The bivariate relationships are significant:
(i) Telepresence and Enjoyment (r = .659, p < .001) and (ii) Telepresence and Brand Equity (r = .373, p < .001).
Satisfied
Brand Equity = b0 + b1 ×
Telepresence + b2 × Enjoyment + e 2D Condition – Satisfied
For step 3, Enjoyment is correlated with Brand Equity, with b2 = .156 (beta = .180, p = .056). Enjoyment is a mediator because it is still (marginally) correlated with Brand Equity even when Telepresence is in the model.
For step 4, the correlation between Telepresence and Brand Equity is such that b1 = .212 (beta = .255, p < .01), which implies that Telepresence and Brand Equity are still correlated even when Enjoyment is in the model.
2D Condition
3D Condition – Satisfied
The bivariate relationships are significant:
(i) Telepresence and Enjoyment (r = .520, p < .001) and (ii) Telepresence and Brand Equity (r = .394, p < .001).
3D Condition – Satisfied
Step 3 is satisfied with b2 = .424 (beta = .375, p < .001) and step 4 is satisfied with b1 = .187 (beta = .199, p < .01), which implies that Tele-presence and Brand Equity are still correlated even when Enjoyment is in the model. 3D Condition Brand Equity Telepresence Telepresence r = .373, p < .001 r = .659, p < .001 Enjoyment Steps 1 and 2 Brand Equity Enjoyment Telepresence b=.212, ß=.255, p < .01 b=.156, ß=.180, p=.056 Steps 3 and 4 Brand Equity Telepresence Telepresence r = .394, p < .001 r = .520, p < .001 Steps 1 and 2 Enjoyment Brand Equity Enjoyment Telepresence b=.187, ß=.199, p < .01 b=.424, ß=.375, p < .001 Steps 3 and 4
Results from Steps 1 and 2 Results from Steps 3 and 4 Overall Results 2D and 3D – Satisfied
The bivariate relationships are significant:
(i) Telepresence and Enjoyment (r = .601, p < .001) and (ii) Telepresence and Brand Equity (r = .384, p < .001).
2D and 3D – Satisfied
Step 3 is satisfied with b2 = .279 (beta = .287, p < .001) and step 4 is satisfied with b1 = .187 (beta = .212, p < .001), which implies that Tele-presence and Brand Equity are still correlated even when Enjoyment is in the model.
2D and 3D Combined
Therefore, Enjoyment partially mediates the relationship between Telepresence and Brand Equity, and this holds true for both the 2D and 3D conditions separately as well as 2D and 3D com-bined. This result corresponds with the results in Figure 2 in the paper, which also shows that Enjoyment partially mediates the relationship between Telepresence and Brand Equity.
Brand Equity as a Mediator of the Relationship Between Enjoyment and Behavioral Intention
Results from Steps 1 and 2 Results from Steps 3 and 4 Overall ResultsSatisfied
2D Condition – Satisfied
The bivariate relationships are significant:
(i) Enjoyment and Brand Equity (r = .348, p < .001) and (ii) Enjoyment and Behavioral Intention (r = .452, p < .001).
Satisfied
Behavioral Intention = b0 + b1 × Enjoyment + b2 × (Brand Equity) + e
2D Condition – Satisfied
For step 3, Brand Equity is correlated with Behavioral Intention with b2 = .354 (beta = .257, p < .001). Therefore, Brand Equity is a mediator because it is still correlated with Behavioral Intention even when Enjoyment is in the model. For step 4, the correlation between Enjoyment and Behavioral Intention is such that b1 = .431 (beta = .366, p < .001), which implies that Enjoyment and Behavioral Intention are still correlated even when Brand Equity is in
2D Condition Brand Equity Telepresence Telepresence r = .384, p < .001 r = .601, p < .001 Steps 1 and 2 Enjoyment Brand Equity Enjoyment Telepresence b=.187, ß=.212, p < .001 b=.279, ß=.287, p < .001 Steps 3 and 4 Behavioral Intention Enjoyment Enjoyment r = .452, p < .001 r = .348, p < .001 Steps 1 and 2 Brand Equity Behavioral Intention Enjoyment b=.431, ß=.366, p < .001 b=.354, ß=.257, p < .001 Steps 3 and 4 Brand Equity
Results from Steps 1 and 2 Results from Steps 3 and 4 Overall Results 3D Condition – Satisfied
The bivariate relationships are significant:
(i) Enjoyment and Brand Equity (r = .479, p < .001) and (i) Enjoyment and Behavioral Intention (r = .508, p < .001).
3D Condition – Satisfied
Step 3 is satisfied with b2 = .435 (beta = .356, p < .001) and step 4 is satisfied with b1 = .473 (beta = .345, p < .001), which implies that Enjoyment and Behavioral Intention are still correlated even when Brand Equity is in the model.
3D Condition
2D and 3D – Satisfied
The bivariate relationships are significant:
(i) Enjoyment and Brand Equity (r = .414, p < .001) and (ii) Enjoyment and Behavioral Intention (r = .491, p < .001).
2D and 3D – Satisfied
Step 3 is satisfied with b2 = .405 (beta = .314, p < .001) and step 4 is satisfied with b1 = .454 (beta = .371, p < .001), which implies that Enjoyment and Behavioral Intention are still correlated even when Brand Equity is in the model.
2D and 3D Combined
Therefore, Brand Equity partially mediates the relationship between Enjoyment and Behavioral Intention, and this holds true for both the 2D and 3D conditions separately as well as 2D and 3D combined. This result corresponds with the results in Figure 2 in the paper, which also shows that Brand Equity partially mediates the relationship between Enjoyment and Behavioral Intention.
Behavioral Intention Enjoyment Enjoyment r = .508, p < .001 r = .479, p < .001 Steps 1 and 2 Brand Equity Behavioral Intention Enjoyment b=.473, ß=.345, p < .001 b=.435, ß=.356, p < .001 Steps 3 and 4 Brand Equity Behavioral Intention Enjoyment Enjoyment r = .491, p < .001 r = .414, p < .001 Steps 1 and 2 Brand Equity Behavioral Intention Enjoyment b=.454, ß=.371, p < .001 b=.405, ß=.314, p < .001 Steps 3 and 4 Brand Equity
Enjoyment as a Mediator of the Relationship Between Telepresence and Behavioral Intention
Results from Steps 1 and 2 Results from Steps 3 and 4 Overall ResultsSatisfied
2D Condition – Satisfied
The bivariate relationships are significant:
(i) Telepresence and Enjoy-ment (r = .659, p < .001) and (ii) Telepresence and Behavioral Intention (r = .447, p < .001). Satisfied Behavioral Intention = b0 + b1 × Telepresence + b2 × Enjoyment + e 2D Condition – Satisfied
For step 3, Enjoyment is correlated with Behavioral Intention with b2 = .327 (beta = .278, p < .01). Therefore, Enjoyment is a mediator because it is still correlated with Behavioral Intention even when Telepresence is in the model.
For step 4, the correlation between Telepresence and Behavioral Intention is such that b1 = .297 (beta = .262, p < .01), which implies that Telepresence and Behavioral Intention are still correlated even when Enjoyment is in the model.
2D Condition
3D Condition – Satisfied
The bivariate relationships are significant:
(i) Telepresence and Enjoy-ment (r = .520, p < .001) and (ii) Telepresence and Behavioral Intention (r = .459, p < .001).
3D Condition – Satisfied
Step 3 is satisfied with b2 = .510 (beta = .372, p < .001) and step 4 is satisfied with b1 = .314 (beta = .275, p < .001), which implies that Tele-presence and Behavioral Intention are still correlated even when Enjoyment is in the model. 3D Condition Behavioral Intention Telepresence Telepresence r = .447, p < .001 r = .659, p < .001 Steps 1 and 2 Enjoyment Behavioral Intention Enjoyment Telepresence b=.297, ß=.262, p < .01 b=.327, ß=.278, p < .01 Steps 3 and 4 Behavioral Intention Telepresence Telepresence r = .459, p < .001 r = .520, p < .001 Steps 1 and 2 Enjoyment Behavioral Intention Enjoyment Telepresence b=.314, ß=.275, p < .001 b=.510, ß=.372, p < .001 Steps 3 and 4
Results from Steps 1 and 2 Results from Steps 3 and 4 Overall Results 2D and 3D – Satisfied
The bivariate relationships are significant:
(i) Telepresence and Enjoy-ment (r = .601, p < .001) and (ii) Telepresence and Behavioral Intention (r = .466, p < .001).
2D and 3D – Satisfied
Step 3 is satisfied with b2 = .404 (beta = .330, p < .001) and step 4 is satisfied with b1 = .299 (beta = .268, p < .001), which implies that Tele-presence and Behavioral Intention are still correlated even when Enjoyment is in the model.
2D and 3D Combined
Therefore, Enjoyment partially mediates the relationship between Telepresence and Behavioral Intention, and this holds true for both the 2D and 3D conditions separately as well as 2D and 3D combined. This result corresponds with the results in Figure 2 in the paper, which also shows that Enjoyment partially mediates the relationship between Telepresence and Behavioral Intention.
Brand Equity as a Mediator of the Relationship Between Telepresence and Behavioral Intention
Results from Steps 1 and 2 Results from Steps 3 and 4 Overall ResultsSatisfied
2D Condition – Satisfied
The bivariate relationships are significant:
(i) Telepresence and Brand Equity (r = .373, p < .001) and (ii) Telepresence and Behavioral Intention (r = .447, p < .001). Satisfied Behavioral Intention = b0 + b1 × Telepresence + b2 × (Brand Equity) + e 2D Condition – Satisfied
For step 3, Brand Equity is correlated with Behavioral Intention with b2 = .344 (beta = .250, p < .01). Therefore, Brand Equity is a mediator because it is still correlated with Behavioral Intention even when Telepresence is in the model. For step 4, the correlation between Telepresence and Behavioral Intention is such that b = .403 (beta = .356, p < 2D Condition Behavioral Intention Telepresence Telepresence r = .466, p < .001 r = .601, p < .001 Steps 1 and 2 Enjoyment Behavioral Intention Enjoyment Telepresence b=.299, ß=.268, p < .001 b=.404, ß=.330, p < .001 Steps 3 and 4 Behavioral Intention Telepresence r = .447, p < .001 r = .373, p < .001 Brand Equity Steps 1 and 2 Telepresence Behavioral Intention Brand Equity Telepresence b=.403, ß=.356, p < .001 b=.344, ß=.250, p < .01 Steps 3 and 4
Results from Steps 1 and 2 Results from Steps 3 and 4 Overall Results 3D Condition – Satisfied
The bivariate relationships are significant:
(i) Telepresence and Brand Equity (r = .394, p < .001) and (ii) Telepresence and Behavioral Intention (r = .459, p < .001).
3D Condition – Satisfied
Step 3 is satisfied with b2 = .474 (beta = .389, p < .001) and step 4 is satisfied with b1 = .334 (beta = .292, p < .001), which implies that Telepresence and Behavioral Intention are still correlated even when Brand Equity is in the model.
3D Condition
2D and 3D – Satisfied
The bivariate relationships are significant:
(i) Telepresence and Brand Equity (r = .384, p < .001) and (ii) Telepresence and Behavioral Intention (r = .466, p < .001).
2D and 3D – Satisfied
Step 3 is satisfied with b2 = .416 (beta = .322, p < .001) and step 4 is satisfied with b1 = .379 (beta = .339, p < .001), which implies that Telepresence and Behavioral Intention are still correlated even when Brand Equity is in the model.
2D and 3D Combined
Therefore, Brand Equity partially mediates the relationship between Telepresence and Behavioral Intention, and this holds true for both the 2D and 3D conditions separately as well as 2D and 3D
combined. This result corresponds with the results in Figure 2 in the paper, which also shows that Brand Equity partially mediates the relationship between Telepresence and Behavioral Intention.
Behavioral Intention Telepresence r = .459, p < .001 r = .394, p < .001 Brand Equity Steps 1 and 2 Telepresence Behavioral Intention Brand Equity Telepresence b=.334, ß=.292, p < .001 b=.474, ß=.389, p < .001 Steps 3 and 4 Behavioral Intention Telepresence r = .466, p < .001 r = .384, p < .001 Brand Equity Steps 1 and 2 Telepresence Behavioral Intention Brand Equity Telepresence b=.379, ß=.339, p < .001 b=.416, ß=.322, p < .001 Steps 3 and 4
Telepresence and Enjoyment as Mediators of the Relationship Between 2D/3D and Brand Equity
Results from Steps 1 and 2 Results from Steps 3 and 4 Overall ResultsNot Satisfied
Although the bivariate relation-ship between 2D/3D and Telepresence is significant (r = .204, p < .001) and between 2D/3D and Enjoyment is also significant, (r = .292, p < .001), the bivariate relationship between 2D/3D and Brand Equity is not (p = .471).
The reason for the non-significant relationship between 2D/3D and Brand Equity is due to opposing or counteracting forces taking place between 2D/3D and Brand Equity. Since the first and second mediator tests support positive mediating effects of Telepresence and Enjoyment in the model, the non-significant relationship between 2D/3D and Brand Equity is likely caused by a counteracting, negative effect of 2D/3D on Brand Equity which we attribute to distraction-conflict theory.
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